`for` loops

Enter for loops. for loops are the “OG” form of iteration in computer science. The basic syntax is below. Basically, we can use a for loop to loop through and print a series of things.

for(i in letters[1:5]){
  print(i)
}

## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"

The code above “loops” through 5 times, printing the iteration letter.

`_apply()` family

A somewhat faster version of for loops comes from the _apply() family of functions, including: apply(), lapply(), sapply(), and mapply(). Unlike for loops, these are vectorized, which makes them more efficient.

lapply(letters[1:5], print)

## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"

## [[1]]
## [1] "a"
## 
## [[2]]
## [1] "b"
## 
## [[3]]
## [1] "c"
## 
## [[4]]
## [1] "d"
## 
## [[5]]
## [1] "e"

sapply(letters[1:5], print)

## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"

##   a   b   c   d   e 
## "a" "b" "c" "d" "e"

mapply(print, letters[1:5])

## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"

##   a   b   c   d   e 
## "a" "b" "c" "d" "e"

`purrr` and `_map_()` functions

Today, though, we’ll focus on the map() family of functions, which is the functions through which purrr iterates.

map(letters[1:5], print)

## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"

## [[1]]
## [1] "a"
## 
## [[2]]
## [1] "b"
## 
## [[3]]
## [1] "c"
## 
## [[4]]
## [1] "d"
## 
## [[5]]
## [1] "e"

For a more thorough comparison of for loops, the _apply() family, and _map_() functions, see https://jennybc.github.io/purrr-tutorial/

`purrr` and `_map_()` predicates

Today, though, we’ll focus on the map() family of functions, which is the functions through which purrr iterates.

map(letters[1:5], print)

Note that this returns a list, which we may not always want. With purrr, we can change the kind of output of map() by adding a predicate, like lgl, dbl, chr, and df. So in the example above, we may have wanted just the characters to print. To do that we’d call map_chr():

map_chr(letters[1:5], print)

## [1] "a"
## [1] "b"
## [1] "c"
## [1] "d"
## [1] "e"

## [1] "a" "b" "c" "d" "e"

Note that it also returns the concatenated character vector as well as printing each letter individually (i.e. iteratively).

`purrr` and `_map_()` antecedents

Single mapping: map_()
Parallel (2) mapping(s): map2_()
3 or more mappings: pmap_()

map2_chr(letters[1:5], 1:5, paste)

## [1] "a 1" "b 2" "c 3" "d 4" "e 5"

Note here that we can use map2() and pmap() with the predicates from above.

Use Cases

Reading Data
Running Models
(Plotting Figures - Kendra Smith, March 20)
(Creating Tables - Emorie Beck (Me), April 17)

Reading Data

There are a number of different cases where purrr and map() maybe useful for reading in data including:

subject-level files for an experimental task
subject- and task-level files for an experimental task
EMA data
longitudinal data
web scraping and text mining

Reading Data: Subject-Level EMA

For this first example, I’ll show you how this would look with a for loop before I show you how it looks with purrr.

Assuming you have all the data in a single folder and the format is reasonably similar, you have the following basic syntax:

data_path <- ""
files <- list.files(data_path)
data <- list()
for(i in files){
  data[[i]] <- read.csv(i, stringsAsFactors = F)
}
data <- combine(data)

Reading Data: Subject-Level EMA

This works fine in this simple case, but where purrr really shines in when you need to make modifications to your data before combining, whether this be recoding, removing missing cases, or renaming variables.

But first, the simple case of reading data. The code below will download a .zip file when you run it. Once, you do, navigate to your Desktop to unzip the folder. You should now be able to run the rest of the code.

data_source <- "https://github.com/emoriebeck/R-tutorials/raw/master/wustl_r_workshops/purrr.zip"
data_dest <- "~/Desktop/purrr.zip"
download.file(data_source, data_dest)

data_path <- "~/Desktop/purrr"
df1 <- tibble(
  ID = list.files(sprintf("%s/data/example_1", data_path))
  ) %>%
  mutate(data = map(ID, ~read_csv(sprintf("%s/data/example_1/%s", data_path, .)))) %>%
  unnest(data)

The code above creates a list of ID’s from the data path (files named for each person), reads the data in using the map() function from purrr, removes the “.csv” from the ID variable, then unnests the data, resulting in a data frame for each person.

List Columns and the Power of `purrr`

On the previous slide, we saw a data frame inside of a data frame. This is called a list column within a nested data frame.

In this case, we created a list column using map, but one of the best things about purrr is how it combines with the nest() and unnest() functions from the tidyr package.

We’ll return to nest() later to demonstrate how anything you would iterate across is also something we can nest() by in long format data frames.

Exercise: Reading in Subject-Level Data

Now, we’re going to combine with what we learned about last time with codebooks.

data_path <- "~/Desktop/purrr"
codebook <- sprintf("%s/data/codebook_ex1.csv", data_path) %>% read_csv(.)
codebook

Exercise: What next?

Now, that we have a codebook, what are the next steps?

df1 <- tibble(
  ID = list.files(sprintf("%s/data/example_1", data_path))
  ) %>%
  mutate(data = map(ID, ~read_csv(sprintf("%s/data/example_1/%s", data_path, .)))) %>%
  unnest(data) 
df1

pull old names in raw data from codebook
pull new names from codebook
select columns from codebook in loaded data
rename columns with new names

Exercise: Reading in Subject-Level Data

old.names <- codebook$old_name # pull old names in raw data from codebook  
new.names <- codebook$new_name # pull new names from codebook  
df1 <- tibble(
  ID = list.files(sprintf("%s/data/example_1", data_path))
  ) %>%
  mutate(data = map(ID, ~read_csv(sprintf("%s/data/example_1/%s", data_path, .)))) %>%
  unnest(data) %>%
  select(ID, count, one_of(old.names)) %>% # select columns from codebook in loaded data  
  setNames(c("ID", "count", new.names)) # rename columns with new names  
df1

Descriptives: Within-Person Correlations

With these kinds of data, the first thing, we may want to do is look at within-person correlations, which we can do with purrr.

(nested.r <- df1 %>%
  group_by(ID) %>%
  nest() %>%
  mutate(r = map(data, ~cor((.) %>% select(-count), use = "pairwise"))))

We can access it like a list:

nested.r$data[[1]]

Models

To run separate models for each trait, we’ll need to reshape our data.

df.long <- df1 %>%
  gather(key = item, value = value, -count, -ID, -satisfaction, na.rm = T)
df.long

Models

To create composites, we’ll separate traits from items.

df.long <- df1 %>%
  gather(key = item, value = value, -count, -ID, -satisfaction, na.rm = T) %>%
  separate(item, c("Trait",  "item"), sep = "_")
df.long

Models

To create composites, we’ll then group_by() trait, count, and ID and calculate the composites using summarize()

df.long <- df1 %>%
  gather(key = item, value = value, -count, -ID, -satisfaction, na.rm = T) %>%
  separate(item, c("Trait",  "item"), sep = "_") %>%
  group_by(ID, count, Trait) %>%
  summarize(value = mean(value)) 
df.long

Models

Then we’ll get within-person centered values using scale().

df.long <- df1 %>%
  gather(key = item, value = value, -count, -ID, -satisfaction, na.rm = T) %>%
  separate(item, c("Trait",  "item"), sep = "_") %>%
  group_by(ID, count, Trait, satisfaction) %>%
  summarize(value = mean(value)) %>%
  group_by(ID, Trait) %>%
  mutate(value_c = as.numeric(scale(value, center = T, scale = F)))
df.long

Models

And grand-mean centered within-person averages

df.long <- df.long %>%
  group_by(ID, Trait) %>%
  summarize(value_gmc = mean(value)) %>%
  group_by(Trait) %>%
  mutate(value_gmc = as.numeric(scale(value_gmc, center = T, scale = F))) %>%
  full_join(df.long)
df.long

Models

And now we are ready to run our models. But first, we’ll nest() our data.

nested.mods <- df.long %>%
  group_by(Trait) %>%
  nest()
nested.mods

Models

And now run the models.

nested.mods <- df.long %>%
  group_by(Trait) %>%
  nest() %>%
  mutate(model = map(data, ~lmer(satisfaction ~ value_c * value_gmc + (1 | ID), data = .)))
nested.mods

Models

And get data frames of the results:

nested.mods <- df.long %>%
  group_by(Trait) %>%
  nest() %>%
  mutate(model = map(data, ~lmer(satisfaction ~ value_c * value_gmc + (1 | ID), data = .)),
         tidy = map(model, ~tidy(., conf.int = T)))
nested.mods

Models: Unnesting

Which we can print into pretty data frames

nested.mods %>%
  select(Trait, tidy) %>%
  unnest(tidy)

Models: Unnesting & Plotting

Which we can pretty easily turn into plots:

nested.mods %>%
  select(Trait, tidy) %>%
  unnest(tidy) %>%
  filter(term == "value_c") %>%
  ggplot(aes(x = Trait, y = estimate, ymin = conf.low, ymax = conf.high)) +
    geom_errorbar(position = "dodge", width = .1) +
    geom_point(aes(color = Trait), size = 2) +
    geom_hline(aes(yintercept = 0), linetype = "dashed") +
    labs(y = "Personality State-\nSatisfaction Association") +
    coord_flip() +
    theme_classic() +
    theme(legend.position = "none")

purrr

Iteration

for loops

_apply() family

purrr and _map_() functions

purrr and _map_() predicates

purrr and _map_() antecedents

Use Cases

Reading Data

Reading Data: Subject-Level EMA

Reading Data: Subject-Level EMA

List Columns and the Power of purrr

Exercise: Reading in Subject-Level Data

Exercise: What next?

Exercise: Reading in Subject-Level Data

Descriptives: Within-Person Correlations

Models

Models

Models

Models

Models

Models

Models

Models

Models: Unnesting

Models: Unnesting & Plotting

`purrr`

`for` loops

`_apply()` family

`purrr` and `_map_()` functions

`purrr` and `_map_()` predicates

`purrr` and `_map_()` antecedents

List Columns and the Power of `purrr`